OpenRaiser/Intern-Atlas huggingface dataset:
Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning
Venue: arXiv cs.CL 2025
Year: 2025

Foundation models have achieved great success in natural language processing (NLP) and computer vision (CV). Their success largely stems from the ability to integrate multi-domain knowledge in pre-training and transfer it to target domains. Considering graph data, especially graphs without textual features, is ubiquitous in real-world applications such as social networks and recommendation systems, some researchers have attempted to extend this paradigm to the graph field, aiming to construct graph foundation models. However, unlike CV and NLP, there are huge gaps among the semantics and properties of graphs in different domains, while current works still adopt traditional contrastive pre-training strategies designed in the single-domain scenario, which regard contrastive samples from different domains as equivalent. From experimental investigations, we discovered that inherent domain-specific differences prevent these strategies from effectively absorbing knowledge from different domains to generate informative representations. In this paper, we propose a novel multi-domain pre-training and cross-domain transfer framework, namely MDGCL.In the pre-training stage, we design a contrastive learning strategy to substantially recognize and capture domain differences, and introduce domain tokens to encode domain-level global information. In the downstream stage, we introduce a domain attention mechanism to enable fine-grained domain knowledge transfer. Extensive experiments on five benchmark datasets have demonstrated that our method outperforms state-of-the-art significantly, with the maximum improvement of 19.33\% on accuracy and 19.13\% on Macro-F1 score.
Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics
Venue: arXiv cs.HC 2025
Year: 2025

This study examines the impact of an Artificial Intelligence tutor teammate (AI) on student curiosity-driven engagement and learning effectiveness during Interactive Molecular Dynamics (IMD) tasks on the Visual Molecular Dynamics platform. It explores the role of the AI's curiosity-triggering and response behaviors in stimulating and sustaining student curiosity, affecting the frequency and complexity of student-initiated questions. The study further assesses how AI interventions shape student engagement, foster discovery curiosity, and enhance team performance within the IMD learning environment. Using a Wizard-of-Oz paradigm, a human experimenter dynamically adjusts the AI tutor teammate's behavior through a large language model. By employing a mixed-methods exploratory design, a total of 11 high school students participated in four IMD tasks that involved molecular visualization and calculations, which increased in complexity over a 60-minute period. Team performance was evaluated through real-time observation and recordings, whereas team communication was measured by question complexity and AI's curiosity-triggering and response behaviors. Cross Recurrence Quantification Analysis (CRQA) metrics reflected structural alignment in coordination and were linked to communication behaviors. High-performing teams exhibited superior task completion, deeper understanding, and increased engagement. Advanced questions were associated with AI curiosity-triggering, indicating heightened engagement and cognitive complexity. CRQA metrics highlighted dynamic synchronization in student-AI interactions, emphasizing structured yet adaptive engagement to promote curiosity. These proof-of-concept findings suggest that the AI's dual role as a teammate and educator indicates its capacity to provide adaptive feedback, sustaining engagement and epistemic curiosity.
MetaCipher: A Time-Persistent and Universal Multi-Agent Framework for Cipher-Based Jailbreak Attacks for LLMs
Venue: arXiv cs.CR 2025
Year: 2025

As large language models (LLMs) grow more capable, they face growing vulnerability to sophisticated jailbreak attacks. While developers invest heavily in alignment finetuning and safety guardrails, researchers continue publishing novel attacks, driving progress through adversarial iteration. This dynamic mirrors a strategic game of continual evolution. However, two major challenges hinder jailbreak development: the high cost of querying top-tier LLMs and the short lifespan of effective attacks due to frequent safety updates. These factors limit cost-efficiency and practical impact of research in jailbreak attacks. To address this, we propose MetaCipher, a low-cost, multi-agent jailbreak framework that generalizes across LLMs with varying safety measures. Using reinforcement learning, MetaCipher is modular and adaptive, supporting extensibility to future strategies. Within as few as 10 queries, MetaCipher achieves state-of-the-art attack success rates on recent malicious prompt benchmarks, outperforming prior jailbreak methods. We conduct a large-scale empirical evaluation across diverse victim models and benchmarks, demonstrating its robustness and adaptability. Warning: This paper contains model outputs that may be offensive or harmful, shown solely to demonstrate jailbreak efficacy.
Directed Shape Morphing using Kirigami-enhanced Thermoplastics
Venue: arXiv cs.RO 2025
Year: 2025

We present a simple, accessible method for autonomously transforming flat plastic sheets into intricate three-dimensional structures using only uniform heating and common tools such as household ovens and scissors. Our approach combines heat-shrinkable thermoplastics with Kirigami patterns tailored to the target 3D shape, creating bilayer composites that morph into a wide range of complex structures, e.g., bowls, pyramids, and even custom ergonomic surfaces like mouse covers. Critically, the transformation is driven by a low-information stimulus (uniform heat) yet produces highly intricate shapes through programmed geometric design. The morphing behavior, confirmed by finite element simulations, arises from strain mismatch between the contracting thermoplastic layer and the constraining Kirigami layer. By decoupling material composition from mechanical response, this method avoids detailed process control and enables a broad class of self-morphing structures, offering a versatile platform for adaptive design and scalable manufacturing.
From MBQI to Enumerative Instantiation and Back
Venue: arXiv cs.LO 2025
Year: 2025

This work investigates the relation between model-based quantifier instantiation (MBQI) and enumerative instantiation (EI) in Satisfiability Modulo Theories (SMT). MBQI operates at the semantic level and guarantees to find a counterexample to a given a non-model. However, it may lead to weak instantiations. In contrast, EI strives for completeness by systematically enumerating terms at the syntactic level. However, such terms may not be counter-examples. Here we investigate the relation between the two techniques and report on our initial experiments of the proposed algorithm that combines the two.
Layer Importance for Mathematical Reasoning is Forged in Pre-Training and Invariant after Post-Training
Venue: arXiv cs.LG 2025
Year: 2025

Large language models improve at math after instruction tuning, reinforcement learning, or knowledge distillation. We ask whether these gains come from major changes in the transformer layers or from smaller adjustments that keep the original structure. Using layer-wise ablation on base and trained variants, we find that math reasoning depends on a few critical layers, which stay important across all post-training methods. Removing these layers reduces math accuracy by as much as 80%, whereas factual recall tasks only show relatively smaller drops. This suggests that specialized layers for mathematical tasks form during pre-training and remain stable afterward. As measured by Normalized Mutual Information (NMI), we find that near these critical layers, tokens drift from their original syntactic clusters toward representations aligned with tokens less syntactically related but potentially more useful for downstream task.
URSA: The Universal Research and Scientific Agent
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) have moved far beyond their initial form as simple chatbots, now carrying out complex reasoning, planning, writing, coding, and research tasks. These skills overlap significantly with those that human scientists use day-to-day to solve complex problems that drive the cutting edge of research. Using LLMs in "agentic" AI has the potential to revolutionize modern science and remove bottlenecks to progress. In this work, we present URSA, a scientific agent ecosystem for accelerating research tasks. URSA consists of a set of modular agents and tools, including coupling to advanced physics simulation codes, that can be combined to address scientific problems of varied complexity and impact. This work highlights the architecture of URSA, as well as examples that highlight the potential of the system.
CAD-Integrated Electrostatic Boundary Element Simulations with Non-Conforming Higher-Order Meshes
Venue: arXiv cs.CE 2025
Year: 2025

We present a design through analysis workflow that enables virtual prototyping of electric devices. A CAD plugin establishes the interaction between design and analysis, allowing the preparation of analysis models and the visualization of its results within the design environment. The simulations utilize a fast boundary element method (BEM) that allows for non-conforming and higher-order meshes. Our numerical experiments investigate the accuracy of the approach and its sensitivity to the initial CAD representation. Overall, the workflow enables a close link between design and analysis, where the non-conforming higher-order BEM approach provides accurate results and significantly simplifies the interaction.
3D Shape Generation: A Survey
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state-of-the-art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.
Compositional Control-Driven Boolean Circuits
Venue: arXiv cs.LO 2025
Year: 2025

Boolean circuits abstract away from physical details to focus on the logical structure and computational behaviour of digital components. Despite they have been studied for many decades, compositionality has been widely ignored or examined in an informal manner, which is a property for combining circuits without delving into their internal structure, while supporting modularity and formal reasoning. In this paper, we address this longstanding theoretical gap by proposing colimit-based operators for compositional circuit construction. We define separate operators for forming sequential, parallel, branchial and iterative circuits. As composites encapsulate explicit control flow, a new model of computation emerges which we refer to as (families of) control-driven Boolean circuits. We show how this model is at least as powerful as its classical counterpart. In other words, it is able to non-uniformly compute any Boolean function on inputs of arbitrary length.
Part Segmentation and Motion Estimation for Articulated Objects with Dynamic 3D Gaussians
Venue: arXiv cs.CV 2025
Year: 2025

Part segmentation and motion estimation are two fundamental problems for articulated object motion analysis. In this paper, we present a method to solve these two problems jointly from a sequence of observed point clouds of a single articulated object. The main challenge in our problem setting is that the point clouds are not assumed to be generated by a fixed set of moving points. Instead, each point cloud in the sequence could be an arbitrary sampling of the object surface at that particular time step. Such scenarios occur when the object undergoes major occlusions, or if the dataset is collected using measurements from multiple sensors asynchronously. In these scenarios, methods that rely on tracking point correspondences are not appropriate. We present an alternative approach based on a compact but effective representation where we represent the object as a collection of simple building blocks modeled as 3D Gaussians. We parameterize the Gaussians with time-dependent rotations, translations, and scales that are shared across all time steps. With our representation, part segmentation can be achieved by building correspondences between the observed points and the Gaussians. Moreover, the transformation of each point across time can be obtained by following the poses of the assigned Gaussian (even when the point is not observed). Experiments show that our method outperforms existing methods that solely rely on finding point correspondences. Additionally, we extend existing datasets to emulate real-world scenarios by considering viewpoint occlusions. We further demonstrate that our method is more robust to missing points as compared to existing approaches on these challenging datasets, even when some parts are completely occluded in some time-steps. Notably, our part segmentation performance outperforms the state-of-the-art method by 13% on point clouds with occlusions.
Memory as a Service (MaaS): Rethinking Contextual Memory as Service-Oriented Modules for Collaborative Agents
Venue: arXiv cs.HC 2025
Year: 2025

This position paper aims to rethink the role and design of memory in Large Language Model (LLM)-based agent systems. We observe that while current memory practices have begun to transcend the limitations of single interactions, they remain conceptually grounded in "bound memory" in terms of design concept-where memory is treated as local state attached to specific context or entities, forming "memory silos" that impede cross-entity collaboration. To overcome this architectural bottleneck, this paper proposes the timely design perspective of "Memory as a Service" (MaaS). MaaS advocates decoupling memory from its conventional role as an interaction byproduct and encapsulating it as a modular service that can be independently callable, dynamically composable, and finely governed. At its core, MaaS leverages the duality of memory-its inherently private nature and its potential for public service-to grant memory controlled, on-demand interoperability across entities. This paper introduces a two-dimensional design space defined by entity structure and service type, illustrating how MaaS aligns with current memory practices while naturally extending them to cross-entity collaborative scenarios. Finally, we outline an open research agenda spanning governance, security, and ethical ecosystems, and call upon the broader research community to explore this shift toward service-oriented memory for collaborative agents operating across entity boundaries.
DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues
Venue: arXiv cs.CL 2025
Year: 2025

Existing function-calling benchmarks focus on single-turn interactions. However, they overlook the complexity of real-world scenarios. To quantify how existing benchmarks address practical applications, we introduce DICE-SCORE, a metric that evaluates the dispersion of tool-related information such as function name and parameter values throughout the dialogue. Analyzing existing benchmarks through DICE-SCORE reveals notably low scores, highlighting the need for more realistic scenarios. To address this gap, we present DICE-BENCH, a framework that constructs practical function-calling datasets by synthesizing conversations through a tool graph that maintains dependencies across rounds and a multi-agent system with distinct personas to enhance dialogue naturalness. The final dataset comprises 1,607 high-DICE-SCORE instances. Our experiments on 19 LLMs with DICE-BENCH show that significant advances are still required before such models can be deployed effectively in real-world settings. Our code and data are all publicly available: https://snuhcc.github.io/DICE-Bench/.
Mind the Gap: Entity-Preserved Context-Aware ASR Structured Transcriptions
Venue: arXiv cs.CL 2025
Year: 2025

Automatic Speech Recognition (ASR) systems, such as Whisper, achieve high transcription accuracy but struggle with named entities and numerical data, especially when proper formatting is required. These issues increase word error rate (WER) and impair semantic understanding in critical domains like legal, financial, and medical applications. We propose a novel training approach that extends the semantic context of ASR models by adding overlapping context windows during training. By sliding 5-second overlaps on both sides of 30-second chunks, we create a 40-second "effective semantic window," improving entity recognition and formatting while focusing predictions on the central 30 seconds. To address entities spanning chunk boundaries, we reassign such entities entirely to the right-hand chunk, ensuring proper formatting. Additionally, enriched training data with embedded entity labels enables the model to learn both recognition and type-specific formatting. Evaluated on the Spoken Wikipedia dataset, our method improves performance across semantic tasks, including named entity recognition (NER) and entity formatting. These results highlight the effectiveness of context-aware training in addressing ASR limitations for long-form transcription and complex entity recognition tasks.
P$^2$U: Progressive Precision Update For Efficient Model Distribution
Venue: arXiv cs.LG 2025
Year: 2025

Efficient model distribution is becoming increasingly critical in bandwidth-constrained environments. In this paper, we propose a simple yet effective approach called Progressive Precision Update (P$^2$U) to address this problem. Instead of transmitting the original high-precision model, P$^2$U transmits a lower-bit precision model, coupled with a model update representing the difference between the original high-precision model and the transmitted low precision version. With extensive experiments on various model architectures, ranging from small models ($1 - 6$ million parameters) to a large model (more than $100$ million parameters) and using three different data sets, e.g., chest X-Ray, PASCAL-VOC, and CIFAR-100, we demonstrate that P$^2$U consistently achieves better tradeoff between accuracy, bandwidth usage and latency. Moreover, we show that when bandwidth or startup time is the priority, aggressive quantization (e.g., 4-bit) can be used without severely compromising performance. These results establish P$^2$U as an effective and practical solution for scalable and efficient model distribution in low-resource settings, including federated learning, edge computing, and IoT deployments. Given that P$^2$U complements existing compression techniques and can be implemented alongside any compression method, e.g., sparsification, quantization, pruning, etc., the potential for improvement is even greater.
Coordinated 2D-3D Visualization of Volumetric Medical Data in XR with Multimodal Interactions
Venue: arXiv cs.HC 2025
Year: 2025

Volumetric medical imaging technologies produce detailed 3D representations of anatomical structures. However, effective medical data visualization and exploration pose significant challenges, especially for individuals with limited medical expertise. We introduce a novel XR-based system with two key innovations: (1) a coordinated visualization module integrating Multi-layered Multi-planar Reconstruction with 3D mesh models and (2) a multimodal interaction framework combining hand gestures with LLM-enabled voice commands. We conduct preliminary evaluations, including a 15-participant user study and expert interviews, to demonstrate the system's abilities to enhance spatial understanding and reduce cognitive load. Experimental results show notable improvements in task completion times, usability metrics, and interaction effectiveness enhanced by LLM-driven voice control. While identifying areas for future refinement, our findings highlight the potential of this immersive visualization system to advance medical training and clinical practice. Our demo application and supplemental materials are available for download at: https://osf.io/bpjq5/.
Towards Time Series Generation Conditioned on Unstructured Natural Language
Venue: arXiv cs.LG 2025
Year: 2025

Generative Artificial Intelligence (AI) has rapidly become a powerful tool, capable of generating various types of data, such as images and text. However, despite the significant advancement of generative AI, time series generative AI remains underdeveloped, even though the application of time series is essential in finance, climate, and numerous fields. In this research, we propose a novel method of generating time series conditioned on unstructured natural language descriptions. We use a diffusion model combined with a language model to generate time series from the text. Through the proposed method, we demonstrate that time series generation based on natural language is possible. The proposed method can provide various applications such as custom forecasting, time series manipulation, data augmentation, and transfer learning. Furthermore, we construct and propose a new public dataset for time series generation, consisting of 63,010 time series-description pairs.
GamerAstra: Supporting 2D Non-Twitch Video Games for Blind and Low-Vision Players through a Multi-Agent Framework
Venue: arXiv cs.HC 2025
Year: 2025

Blind and low-vision (BLV) players face critical challenges in engaging with video games due to the inaccessibility of visual elements, difficulties navigating interfaces, and limitations in performing interaction. Meanwhile, the development of specialized accessibility features typically requires substantial programming effort and is often implemented on a game-by-game basis. To address these challenges, we introduce GamerAstra, a multi-agent human-AI collaboration framework that leverages a multi-agent design to facilitate access to 2D non-twitch video games for BLV players. It integrates vision-language models and computer vision techniques, enabling interaction with games lacking native accessibility support. The framework also incorporates custom assistance granularities to support varying degrees of visual impairment and enhances interface navigation through multiple input modalities. Technical evaluations and user studies indicate that GamerAstra effectively enhances playability and provides a more immersive gaming experience for BLV players. These findings also underscore potential avenues for advancing intelligent accessibility frameworks in the gaming domain.